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Toni KarvonenView profile

Assistant Professor

• Statistics and Probability
• Applied Mathematics
• Numerical Analysis
• +10 more

Toni Karvonen is an Assistant Professor of Applied Mathematics at LUT University's Computational Engineering Department, School of Engineering Sciences, since September 2024. His research focuses on statistics, applied mathematics, and computational methods with emphasis on Gaussian processes, uncertainty quantification, and kernel-based approximation. He has held postdoctoral positions at the Alan Turing Institute (2020–21) and the University of Helsinki (2021–24). Research Areas: Statistics and Probability, Applied Mathematics, Numerical Analysis, Gaussian Processes, Positive Definite Kernels Key Trends: His recent work explores probabilistic numerics, Bayesian quadrature, kernel methods for uncertainty quantification, and approximation theory in reproducing kernel Hilbert spaces. Email: Toni.Karvonen@lut.fi

Heikki KälviäinenView profile

Professor

• Computer Vision
• Machine Vision
• Pattern Recognition
• +4 more

Professor Heikki Kälviäinen is a distinguished academic at Lappeenranta-Lahti University of Technology LUT, where he serves as Professor of Computer Science and Engineering in the School of Engineering Sciences. He leads the Computer Vision and Pattern Recognition Laboratory (CVPRL) within the Department of Computational Engineering. Additionally, he has an upcoming appointment as Professor of Computer Science and Engineering at Brno University of Technology's Faculty of Information Technology from January 2025 to January 2028. Professor Kälviäinen received his Doctor of Science (Technology) degree in 1994 from Lappeenranta University of Technology. His academic journey includes significant positions beyond LUT, such as Professor of Computing at Monash University Malaysia's School of Information Technology, and Visiting Professor roles at Brno University of Technology, Czech Technical University's Center for Machine Perception, and University of Surrey's Center for Vision, Speech, and Signal Processing. Professor Kälviäinen's research focuses on computer vision, machine vision, pattern recognition, machine learning, and digital image processing and analysis. His work has significant applications in diverse fields including wildlife conservation (particularly animal re-identification), plankton recognition, and industrial applications like timber and wood species identification. His research demonstrates a strong bridge between theoretical computer vision techniques and practical real-world applications. His publication record is extensive, with over 230 peer-reviewed scientific articles, 49 other research publications, and leadership in 51 research projects totaling 6.3 million EUR. His work has achieved an h-index of 38 with over 6500 citations, reflecting substantial impact in his fields of expertise. Professor Kälviäinen has served as supervisor for 28 doctoral and 173 Master's theses, demonstrating his commitment to academic mentorship. He has also served as a Ph.D. thesis reviewer 35 times across 8 different countries, contributing to international academic quality assurance. His leadership extends to various administrative roles including Vice Dean, Head of Department/Degree Program/Laboratory at LUT, Director of East Finland Graduate School in Computer Science and Engineering (ECSE), and Deputy Head of School at Monash University Malaysia.

• Quantum Physics
• Condensed Matter Physics
• Computational Methods
• +2 more

Pascal Vecsei is a Visiting Faculty member at Aalto University's Department of Applied Physics and a Doctoral Researcher at the Centre of Excellence in Quantum Technology (QTF). He holds a Doctoral Degree (Tekn. toht.) in Teknillinen Fysiikka from Aalto University (awarded 2025) and a Master's in Natural Sciences from Universität Zürich (2021). His research focuses on quantum phase transitions, topological materials, and computational methods in condensed matter physics. Doctoral Degree in Technical Physics (2025), Aalto University Master's in Natural Sciences (2021), Universität Zürich Vecsei's work centers on quantum phase transitions using advanced techniques like Lee-Yang formalism , tensor networks , and neural network quantum states . His studies on topological magnets and interacting fermion systems contribute to understanding critical behavior in quantum materials. His publications include studies on 2D quantum Ising models , topological quantum magnets , and fermionic phase diagrams , with a recurring emphasis on phase transitions and critical phenomena. Key collaborations include researchers like Jani Lado and Christian Flindt. Vecsei has contributed to Physical Review B and Physical Review Research , with open-access outputs attracting significant downloads and citations. His work intersects quantum physics, computational methods, and condensed matter systems.

• High Energy Physics
• Experimental Particle Physics
• Medical Imaging
• +3 more

Erik Brücken is a University Lecturer and Docent at the Department of Physics, University of Helsinki. He actively participates in the Doctoral Programme in Materials Research and Nanosciences, as well as the Doctoral Programme in Particle Physics and Universe Sciences. His research spans high-energy physics, experimental particle physics, and detector development. Primary affiliation: Department of Physics, University of Helsinki Supervisory roles in two doctoral programs Active in CMS and ALICE collaborations Research Interests: Brücken's work focuses on experimental particle physics at colliders like the LHC, particularly in detector optimization and data analysis. His projects include the ALICE Time Projection Chamber Upgrade and multispectral photon-counting for medical imaging. Current research examines B meson decays, Higgs boson properties, and jet substructure in heavy-ion collisions. Recent Publications: His 2025 contributions include analyses of B0 meson decays, Higgs self-coupling constraints, top quark production cross-sections, and jet substructure studies in nuclear collisions. Projects: He leads a 2024-2027 European particle physics infrastructure project (TOTEM) and participates in collaborations related to detector development and beam characterization. Past projects include CMS Tracker upgrades and CDF experiments spanning 2002-2017. Academic Activities: Brücken organizes and presents at international conferences like the European Researcher's Night and Euroschool on Exotic Beams. He serves as a doctoral thesis committee member and contributes to peer review processes.

Indrė ŽliobaitėView profile

Professor

• Data Science
• Machine Learning
• Life Science Informatics
• +3 more

Indrė Žliobaitė is a Professor at the Department of Computer Science, Faculty of Science, University of Helsinki, and holds a Docent title in the same department. She maintains dual affiliations with the Department of Geosciences and Geography and serves as supervisor for both the Doctoral Programme in Geosciences and Doctoral Programme in Computer Science. Her research integrates data science and machine learning with earth sciences, focusing on evolutionary paleontology, macroecology, and macroevolution. She develops computational frameworks for analyzing fossil records, modeling species longevity, and reconstructing paleoenvironments, with specific applications in habitat suitability prediction and ecometric inference. Her work bridges algorithmic innovation with paleontological data interpretation. Recent publications reveal a cohesive trajectory where machine learning techniques—particularly probabilistic modeling and Bayesian approaches—are systematically applied to paleontological questions. This includes modeling fossil mammal community dynamics, inferring historical climate impacts on species distribution, and establishing quantitative laws for macroevolutionary processes, demonstrating how computational methods can unlock patterns in deep-time ecological data. Žliobaitė actively supervises doctoral candidates through two university programmes and leads the Academy of Finland project "Macroevolution of ecological relationships" (2023-2027). She collaborates internationally with the Turkana Basin Institute, Staatliches Museum Naturkunde Stuttgart, and Institut Catala de Paleontologia, and manages the New and Old Worlds Mammal Fossil Database (NOW), a critical infrastructure for paleontological research.

Elio NushiView profile

Researcher

• Data Science
• Machine Learning
• Algorithms
• +3 more

Elio Nushi is a Doctoral Researcher at the University of Helsinki , affiliated with the Faculty of Veterinary Medicine's Department of Food Hygiene and Environmental Health. His academic journey includes M.Sc. and B.Sc. degrees in Computer Science with focus on Data Science and Machine Learning. B.Sc., University of New York Tirana (2017) M.Sc., Åbo Akademi University (2020) Ph.D. candidate, University of Helsinki (2025 expected) Nushi develops probabilistic models for time series analysis of multi-omics data in precision medicine. His research integrates network science with computational biology to identify personalized drug combinations, particularly in multiple myeloma studies. Current work involves microbiome modeling and legacy transcriptomic data reanalysis using Bayesian methods. Recent publications highlight his contributions to Bayesian time annotation techniques and network modeling approaches for biomedical applications. He has taught Data Analysis with Python for three consecutive years at the University of Helsinki while maintaining active research in mathematical applications to biological systems.

• Probabilistic Machine Learning
• Statistical Inference
• Computational Neuroscience
• +4 more

Luigi Acerbi is an Associate Professor at the Department of Computer Science, University of Helsinki, leading the Machine and Human Intelligence research group. He is affiliated with the Finnish Center for Artificial Intelligence (FCAI) and ELLIS (European Laboratory for Learning and Intelligent Systems). His research focuses on probabilistic machine learning, statistical inference methods (e.g., amortized and surrogate-based approaches), and computational and cognitive neuroscience, including Bayesian models of perception and resource-constrained rationality. Previously, he held postdoctoral positions at the University of Geneva and New York University. He earned his PhD from the Doctoral Training Centre in Computational Neuroscience at the University of Edinburgh, working with Sethu Vijayakumar and Daniel Wolpert. His work includes developing open-source tools like BADS (Bayesian Adaptive Direct Search) and VBMC (Variational Bayesian Monte Carlo), widely used for optimization and Bayesian inference in MATLAB/Python. He actively contributes to the academic community through teaching (e.g., BAMB! 2022 summer school tutorials on model fitting) and software development (GitHub repositories for optimization, inference, and AI tools like Athanor). His research bridges machine learning, neuroscience, and cognitive science, emphasizing robust and efficient statistical methods.

Dorina RajanenView profile

Lecturer

• Human-Computer Interaction (HCI)
• Climate Change Communication
• Data Mining and Visualization
• +8 more

Dorina Rajanen is a University Lecturer and researcher at the Interact Research Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu. She holds a PhD in Information Systems from Åbo Akademi University (2008) and completed postdoctoral research at Aalto University School of Business. Her work focuses on Human-Computer Interaction (HCI), climate change communication, media psychology, and gamification. She teaches courses on UX evaluation, digital cultures, and research methods. Research interests include climate change perceptions shaped by media, psychophysiological studies of media engagement, and safety culture in digital fabrication. She leads projects funded by the Wihuri Foundation and has published widely in HCI and data mining. Notable awards include the 2022 Best Reviewer Award and 2021 Most Exemplary Course distinction. Her recent articles explore gamification for climate engagement, VR technology acceptance, and safety protocols in maker spaces. Supervised over 30 theses on topics like sustainable design, wearable devices, and cloud computing adoption. Active in academic service and has reviewed for journals like Computers in Human Behavior.

• Machine Learning
• Reinforcement Learning
• Online Learning
• +3 more

Mahsa Asadi is a Postdoctoral Researcher in the Department of Computer Science, focusing on theoretical and applied aspects of machine learning. Her research emphasizes online learning, collaborative algorithms, and reinforcement learning, with contributions to regret minimization, state-action equivalence in RL, and distributed multitask learning. She explores intersections between statistical theory and algorithmic design, with applications in autonomous systems and multi-agent coordination. Her work spans foundational topics like concentration inequalities and upper confidence bounds, alongside practical implementations such as the CYRUS 2D simulation team and Miss PacMan AI controller. Notable contributions include model-based reinforcement learning frameworks that exploit structural equivalence properties, and collaborative mean estimation algorithms. Research Themes: Online Learning, Reinforcement Learning, Distributed Systems, Statistical Learning Theory Key Contributions: Human-in-the-loop systems, state-action abstraction techniques, regret-optimal algorithms No formal academic awards or grants are listed in the provided information. Her advising and collaborative work focus on advancing algorithmic foundations with practical implementations in robotics and game AI contexts.

• Interpretability
• Machine Learning
• Data Mining
• +4 more

Martino Ciaperoni is a Postdoctoral Researcher and Adjunct Professor in the Department of Computer Science at Aalto University. He is affiliated with the research group of Adj. Prof. Gionis Aris. His primary focus is on advancing interpretable machine learning, algorithm optimization, and data mining techniques. Ciaperoni holds a doctoral degree (Tekn. toht.) in Computer Science from Aalto University (2024) and a Doctoral degree in Engineering and Technology from Università degli Studi di Roma 'La Sapienza' (2019). His research emphasizes interpretability in AI, multi-label classification, and efficient algorithms for speech recognition and Bayesian networks. Recent work includes exploring the Rashomon set of rule-based models, developing low-memory Viterbi decoding algorithms (SIEVE), and solving the Hadamard decomposition problem. His research spans topics like core decomposition in temporal networks and low-rank matrix approximation. Collaborations include international projects in computational linguistics, knowledge discovery, and algorithm design. He has contributed to open-source software, such as the 'Efficient Exploration of the Rashomon Set' codebase (Zenodo). His work balances theoretical rigor with practical applications, aiming to bridge gaps between complex algorithms and real-world problem-solving.

Janne MartikainenView profile

Professor

• coronary artery disease
• cost-effectiveness
• digital effectiveness
• +6 more

Janne Martikainen is a Professor at the School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland. His research focuses on pharmacoeconomics, health economics, and outcomes research, with a strong emphasis on cost-effectiveness, digital health, and chronic disease management. Research Groups: Pharmacoeconomics and Outcomes Research Group, StopDia, House of Effectiveness Projects: Alzheimer’s disease Follow-Up Study (ALSOVA), HYPE (wellbeing promotion integration), pharmaceutical waste analysis, and diabetes management evaluations Research Interests: His work spans coronary artery disease, knee osteoarthritis, type 1/type 2 diabetes, and health-related quality of life. He employs statistical modeling, microsimulation, and real-world data to assess interventions in Finland. Recent Article Trends: Recent publications highlight machine learning in diabetes prediction, pharmaceutical waste economics, digital health interventions, and chronic disease cost-effectiveness. Many studies utilize Finland’s national registries and focus on real-world settings. Collaborations: Martikainen collaborates with multidisciplinary teams in diabetes, cardiology, and environmental health. His projects often involve partnerships with StopDia, House of Effectiveness, and Finnish healthcare institutions.

• Quantitative Modeling
• Bayesian Statistics
• Spatial Analytics
• +2 more

Henri Wallen is a Researcher at the Arctic Centre, University of Lapland, Finland. His work integrates quantitative modeling, Bayesian statistics, and spatial analytics to study Arctic socio-ecological systems, particularly reindeer herding and climate impacts. Research interests include landcover changes, justice in Arctic governance, and machine learning applications. Key projects involve analyzing historical and modern reindeer management, invasive species impacts, and mining-related social conflicts. His interdisciplinary approach bridges ethnography, archaeology, and policy analysis. Recent publications focus on reindeer osteometry, cultural practices in tourism, and legal reforms. Trends highlight climate adaptation, community-based research, and justice frameworks for sustainable Arctic development. Wallen contributes to EU projects like CHARTER and JustNorth, emphasizing participatory governance.

• Geropsychology
• Wellbeing and health
• Innovation expertise
• +9 more

Tuomas Leisti is a Lecturer and Doctor of Psychology (PsT) at Metropolia University of Applied Sciences, affiliated with the Wellbeing Team 3. His professional roles include teaching psychology and research methods, supervising Master's theses, and contributing to RDI (Research, Development, Innovation) projects. He holds certifications as a licensed psychologist and professional teacher (AmO), with proficiency in Finnish (native), English (excellent), Swedish (average), and German (basic). Leisti's research focuses on geropsychology, wellbeing and health, innovation expertise, cognitive psychology, and the philosophy of mind. His doctoral work explored the role of consciousness in decision-making, with prior experience in human-technology interaction. He is particularly interested in decision-making processes, problem-solving, and the metacognitive skills underlying these activities. Key projects include the Wellbeing Hybridist initiative (2021–2023) and the pan-European COMHOM project (2025–2028), which addresses homelessness through data-driven solutions. His research outputs span over two decades, emphasizing subjective image quality evaluation, cognitive modeling, and quantitative methodologies. Leisti's work integrates empirical and philosophical approaches, addressing topics such as qualia, decision heuristics, and the interplay between conscious thought and judgment. Despite no explicitly listed awards, his extensive publication record reflects sustained contributions to psychological and applied research fields.

• Statistical Machine Learning
• Exploratory Data Analysis
• Data Science
• +4 more

Jaakko Peltonen is a Professor of Statistics and Data Analysis at Tampere University, Faculty of Information Technology and Communication Sciences, Department of Computing Sciences. He leads the Statistical Machine Learning and Exploratory Data Analysis (SMiLE) research group and is affiliated with the Tampere Research Center in Information and Systems and the Academy of Finland Center of Excellence in Game Culture Studies. His research focuses on statistical machine learning, exploratory data analysis, and their applications in diverse domains including healthcare, social media analytics, gaming, and political discourse analysis. Notable projects include developing algorithms for transparent AI systems (e.g., TraQuLA, Flow of Trust framework) and analyzing online communities through platforms like Twitch and Steam. Recent work emphasizes ethical AI integration in education and healthcare, predictive modeling for emergency department management, and game studies involving player behavior analysis. Peltonen collaborates across disciplines, leveraging text mining, graph theory, and visualization techniques to address complex societal challenges. Key contributions span methodological advancements (e.g., constrained non-negative matrix factorization) and interdisciplinary applications in policy analysis, digital humanities, and health informatics. His work bridges technical innovation with real-world societal impact through collaborative research initiatives.

• Neuroscience
• Computer Science
• Medical Imaging
• +3 more

Susanne Merz is a Researcher affiliated with the School of Science , focusing on interdisciplinary studies at the intersection of neuroscience and computational methods. Her work leverages advanced statistical and artificial intelligence techniques to investigate brain dynamics and cognitive disorders. Her research interests include: Neuroscience Machine Learning Medical Imaging Biostatistics Cognitive Psychology Neuroinformatics Recent publications highlight her contributions to analyzing magnetoencephalography (MEG) data for neural fingerprinting and developing AI-driven tools for dementia risk assessment in mild cognitive impairment (MCI). Collaborations span international institutions, such as the AI-Mind project funded by the EU H2020 Framework program. Her work integrates computational modeling with clinical applications, aiming to improve early detection of neurodegenerative diseases. Scientific contributions: 2024 : Bayesian reduced rank regression and neural fingerprints in MEG 2024 : AI-Mind protocol for brain connectivity screening in MCI Projects: AI-Mind (2021–2026), EU H2020 Framework program Her research employs magnetoencephalography, functional connectivity, and AI to advance understanding of brain networks and their implications for cognitive health.